Search Results for author: Amit Sheth

Found 95 papers, 12 papers with code

Evaluating Biomedical Word Embeddings for Vocabulary Alignment at Scale in the UMLS Metathesaurus Using Siamese Networks

no code implementations insights (ACL) 2022 Goonmeet Bajaj, Vinh Nguyen, Thilini Wijesiriwardene, Hong Yung Yip, Vishesh Javangula, Amit Sheth, Srinivasan Parthasarathy, Olivier Bodenreider

Recent work uses a Siamese Network, initialized with BioWordVec embeddings (distributed word embeddings), for predicting synonymy among biomedical terms to automate a part of the UMLS (Unified Medical Language System) Metathesaurus construction process.

Word Embeddings

Towards Learning Foundation Models for Heuristic Functions to Solve Pathfinding Problems

no code implementations1 Jun 2024 Vedant Khandelwal, Amit Sheth, Forest Agostinelli

Pathfinding problems are found throughout robotics, computational science, and natural sciences.

REASONS: A benchmark for REtrieval and Automated citationS Of scieNtific Sentences using Public and Proprietary LLMs

no code implementations3 May 2024 Deepa Tilwani, Yash Saxena, Ali Mohammadi, Edward Raff, Amit Sheth, Srinivasan Parthasarathy, Manas Gaur

Automatic citation generation for sentences in a document or report is paramount for intelligence analysts, cybersecurity, news agencies, and education personnel.

Hallucination Retrieval +1

Grounding from an AI and Cognitive Science Lens

no code implementations19 Feb 2024 Goonmeet Bajaj, Srinivasan Parthasarathy, Valerie L. Shalin, Amit Sheth

Grounding is a challenging problem, requiring a formal definition and different levels of abstraction.

RDR: the Recap, Deliberate, and Respond Method for Enhanced Language Understanding

no code implementations15 Dec 2023 Yuxin Zi, Hariram Veeramani, Kaushik Roy, Amit Sheth

Natural language understanding (NLU) using neural network pipelines often requires additional context that is not solely present in the input data.

Graph Embedding Natural Language Understanding

Neurosymbolic Value-Inspired AI (Why, What, and How)

no code implementations15 Dec 2023 Amit Sheth, Kaushik Roy

The rapid progression of Artificial Intelligence (AI) systems, facilitated by the advent of Large Language Models (LLMs), has resulted in their widespread application to provide human assistance across diverse industries.

Autonomous Driving Decision Making

Building Trustworthy NeuroSymbolic AI Systems: Consistency, Reliability, Explainability, and Safety

no code implementations5 Dec 2023 Manas Gaur, Amit Sheth

We present the CREST framework that shows how Consistency, Reliability, user-level Explainability, and Safety are built on NeuroSymbolic methods that use data and knowledge to support requirements for critical applications such as health and well-being.

SEPSIS: I Can Catch Your Lies -- A New Paradigm for Deception Detection

no code implementations1 Dec 2023 Anku Rani, Dwip Dalal, Shreya Gautam, Pankaj Gupta, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das

This research explores the problem of deception through the lens of psychology, employing a framework that categorizes deception into three forms: lies of omission, lies of commission, and lies of influence.

Deception Detection Multi-Task Learning

A Cross Attention Approach to Diagnostic Explainability using Clinical Practice Guidelines for Depression

no code implementations23 Nov 2023 Sumit Dalal, Deepa Tilwani, Kaushik Roy, Manas Gaur, Sarika Jain, Valerie Shalin, Amit Sheth

We develop such a system in the context of MH using clinical practice guidelines (CPG) for diagnosing depression, a mental health disorder of global concern.

Clinical Knowledge Language Modelling

L3 Ensembles: Lifelong Learning Approach for Ensemble of Foundational Language Models

no code implementations11 Nov 2023 Aidin Shiri, Kaushik Roy, Amit Sheth, Manas Gaur

Fine-tuning pre-trained foundational language models (FLM) for specific tasks is often impractical, especially for resource-constrained devices.

Language Modelling STS +1

On the Relationship between Sentence Analogy Identification and Sentence Structure Encoding in Large Language Models

1 code implementation11 Oct 2023 Thilini Wijesiriwardene, Ruwan Wickramarachchi, Aishwarya Naresh Reganti, Vinija Jain, Aman Chadha, Amit Sheth, Amitava Das

Through our analysis, we find that LLMs' ability to identify sentence analogies is positively correlated with their ability to encode syntactic and semantic structures of sentences.

Language Modelling Sentence

A Comprehensive Survey on Rare Event Prediction

no code implementations20 Sep 2023 Chathurangi Shyalika, Ruwan Wickramarachchi, Amit Sheth

This paper comprehensively reviews the current approaches for rare event prediction along four dimensions: rare event data, data processing, algorithmic approaches, and evaluation approaches.

A Survey of Hallucination in Large Foundation Models

1 code implementation12 Sep 2023 Vipula Rawte, Amit Sheth, Amitava Das

Hallucination in a foundation model (FM) refers to the generation of content that strays from factual reality or includes fabricated information.


RESTORE: Graph Embedding Assessment Through Reconstruction

no code implementations28 Aug 2023 Hong Yung Yip, Chidaksh Ravuru, Neelabha Banerjee, Shashwat Jha, Amit Sheth, Aman Chadha, Amitava Das

We analyze their effectiveness in preserving the (a) topological structure of node-level graph reconstruction with an increasing number of hops and (b) semantic information on various word semantic and analogy tests.

Graph Embedding Graph Reconstruction +1

Why Do We Need Neuro-symbolic AI to Model Pragmatic Analogies?

no code implementations2 Aug 2023 Thilini Wijesiriwardene, Amit Sheth, Valerie L. Shalin, Amitava Das

A hallmark of intelligence is the ability to use a familiar domain to make inferences about a less familiar domain, known as analogical reasoning.

IERL: Interpretable Ensemble Representation Learning -- Combining CrowdSourced Knowledge and Distributed Semantic Representations

no code implementations24 Jun 2023 Yuxin Zi, Kaushik Roy, Vignesh Narayanan, Manas Gaur, Amit Sheth

Crowdsourced and expert-curated knowledge graphs such as ConceptNet are designed to capture the meaning of words from a compact set of well-defined contexts.

Ensemble Learning Hallucination +3

Knowledge-Infused Self Attention Transformers

no code implementations23 Jun 2023 Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit Sheth

However, the ad-hoc nature of existing methods makes it difficult to properly analyze the effects of knowledge infusion on the many moving parts or components of a transformer.

Knowledge Graphs Language Modelling

Process Knowledge-infused Learning for Clinician-friendly Explanations

no code implementations16 Jun 2023 Kaushik Roy, Yuxin Zi, Manas Gaur, Jinendra Malekar, Qi Zhang, Vignesh Narayanan, Amit Sheth

In this study, we introduce Process Knowledge-infused Learning (PK-iL), a new learning paradigm that layers clinical process knowledge structures on language model outputs, enabling clinician-friendly explanations of the underlying language model predictions.

Explainable Artificial Intelligence (XAI) Language Modelling

Cook-Gen: Robust Generative Modeling of Cooking Actions from Recipes

1 code implementation1 Jun 2023 Revathy Venkataramanan, Kaushik Roy, Kanak Raj, Renjith Prasad, Yuxin Zi, Vignesh Narayanan, Amit Sheth

In this study, we explore the use of generative AI methods to extend current food computation models, primarily involving the analysis of nutrition and ingredients, to also incorporate cooking actions (e. g., add salt, fry the meat, boil the vegetables, etc.).

Food recommendation Nutrition +1

Knowledge Graph Guided Semantic Evaluation of Language Models For User Trust

no code implementations8 May 2023 Kaushik Roy, Tarun Garg, Vedant Palit, Yuxin Zi, Vignesh Narayanan, Amit Sheth

However, they do not ascribe object and concept-level meaning and semantics to the learned stochastic patterns such as those described in knowledge graphs.

Knowledge Graphs Language Modelling

ANALOGICAL -- A Novel Benchmark for Long Text Analogy Evaluation in Large Language Models

no code implementations8 May 2023 Thilini Wijesiriwardene, Ruwan Wickramarachchi, Bimal G. Gajera, Shreeyash Mukul Gowaikar, Chandan Gupta, Aman Chadha, Aishwarya Naresh Reganti, Amit Sheth, Amitava Das

Over the past decade, analogies, in the form of word-level analogies, have played a significant role as an intrinsic measure of evaluating the quality of word embedding methods such as word2vec.

Negation Sentence

FACTIFY-5WQA: 5W Aspect-based Fact Verification through Question Answering

no code implementations7 May 2023 Anku Rani, S. M Towhidul Islam Tonmoy, Dwip Dalal, Shreya Gautam, Megha Chakraborty, Aman Chadha, Amit Sheth, Amitava Das

Finally, we report a baseline QA system to automatically locate those answers from evidence documents, which can serve as a baseline for future research in the field.

Fact Checking Fact Verification +3

Neurosymbolic AI - Why, What, and How

no code implementations1 May 2023 Amit Sheth, Kaushik Roy, Manas Gaur

Humans interact with the environment using a combination of perception - transforming sensory inputs from their environment into symbols, and cognition - mapping symbols to knowledge about the environment for supporting abstraction, reasoning by analogy, and long-term planning.

Autonomous Driving Decision Making +2

"Can We Detect Substance Use Disorder?": Knowledge and Time Aware Classification on Social Media from Darkweb

no code implementations20 Apr 2023 Usha Lokala, Orchid Chetia Phukan, Triyasha Ghosh Dastidar, Francois Lamy, Raminta Daniulaityte, Amit Sheth

We use the Drug Abuse Ontology, state-of-the-art deep learning, and knowledge-aware BERT-based models to generate sentiment and emotion for the social media posts to understand users' perceptions on social media by investigating questions such as: which synthetic opioids people are optimistic, neutral, or negative about?

Factify 2: A Multimodal Fake News and Satire News Dataset

1 code implementation8 Apr 2023 S Suryavardan, Shreyash Mishra, Parth Patwa, Megha Chakraborty, Anku Rani, Aishwarya Reganti, Aman Chadha, Amitava Das, Amit Sheth, Manoj Chinnakotla, Asif Ekbal, Srijan Kumar

In this paper, we provide a multi-modal fact-checking dataset called FACTIFY 2, improving Factify 1 by using new data sources and adding satire articles.

Claim Verification Fact Checking +1

KSAT: Knowledge-infused Self Attention Transformer -- Integrating Multiple Domain-Specific Contexts

no code implementations9 Oct 2022 Kaushik Roy, Yuxin Zi, Vignesh Narayanan, Manas Gaur, Amit Sheth

Domain-specific language understanding requires integrating multiple pieces of relevant contextual information.


Can Language Models Capture Graph Semantics? From Graphs to Language Model and Vice-Versa

no code implementations18 Jun 2022 Tarun Garg, Kaushik Roy, Amit Sheth

Knowledge Graphs are a great resource to capture semantic knowledge in terms of entities and relationships between the entities.

Knowledge Graphs Language Modelling

Process Knowledge-Infused AI: Towards User-level Explainability, Interpretability, and Safety

no code implementations9 Jun 2022 Amit Sheth, Manas Gaur, Kaushik Roy, Revathy Venkataraman, Vedant Khandelwal

For such applications, in addition to data and domain knowledge, the AI systems need to have access to and use the Process Knowledge, an ordered set of steps that the AI system needs to use or adhere to.

Food recommendation Management

MMTM: Multi-Tasking Multi-Decoder Transformer for Math Word Problems

no code implementations2 Jun 2022 Keyur Faldu, Amit Sheth, Prashant Kikani, Darshan Patel

Recently, quite a few novel neural architectures were derived to solve math word problems by predicting expression trees.

Decoder Math +1

Exo-SIR: An Epidemiological Model to Analyze the Impact of Exogenous Spread of Infection

no code implementations3 May 2022 Nirmal Kumar Sivaraman, Manas Gaur, Shivansh Baijal, Sakthi Balan Muthiah, Amit Sheth

In this paper, we introduce the Exo-SIR model, an extension of the popular SIR model and a few variants of the model.

Process Knowledge-infused Learning for Suicidality Assessment on Social Media

no code implementations26 Apr 2022 Kaushik Roy, Manas Gaur, Qi Zhang, Amit Sheth

Improving the performance and natural language explanations of deep learning algorithms is a priority for adoption by humans in the real world.

Explainable Artificial Intelligence (XAI)

Knowledge-based Entity Prediction for Improved Machine Perception in Autonomous Systems

no code implementations30 Mar 2022 Ruwan Wickramarachchi, Cory Henson, Amit Sheth

Knowledge-based entity prediction (KEP) is a novel task that aims to improve machine perception in autonomous systems.

Autonomous Driving

Evaluating Biomedical BERT Models for Vocabulary Alignment at Scale in the UMLS Metathesaurus

no code implementations14 Sep 2021 Goonmeet Bajaj, Vinh Nguyen, Thilini Wijesiriwardene, Hong Yung Yip, Vishesh Javangula, Srinivasan Parthasarathy, Amit Sheth, Olivier Bodenreider

Given the SOTA performance of these BERT models for other downstream tasks, our experiments yield surprisingly interesting results: (1) in both model architectures, the approaches employing these biomedical BERT-based models do not outperform the existing approaches using Siamese Network with BioWordVec embeddings for the UMLS synonymy prediction task, (2) the original BioBERT large model that has not been pre-trained with the UMLS outperforms the SapBERT models that have been pre-trained with the UMLS, and (3) using the Siamese Networks yields better performance for synonymy prediction when compared to using the biomedical BERT models.

Task 2 Word Embeddings

Knowledge-intensive Language Understanding for Explainable AI

no code implementations2 Aug 2021 Amit Sheth, Manas Gaur, Kaushik Roy, Keyur Faldu

To understand and validate an AI system's outcomes (such as classification, recommendations, predictions), that lead to developing trust in the AI system, it is necessary to involve explicit domain knowledge that humans understand and use.

Decision Making Explainable Artificial Intelligence (XAI) +1

Knowledge Infused Policy Gradients with Upper Confidence Bound for Relational Bandits

no code implementations25 Jun 2021 Kaushik Roy, Qi Zhang, Manas Gaur, Amit Sheth

Contextual Bandits find important use cases in various real-life scenarios such as online advertising, recommendation systems, healthcare, etc.

Descriptive Multi-Armed Bandits +2

"Who can help me?": Knowledge Infused Matching of Support Seekers and Support Providers during COVID-19 on Reddit

no code implementations12 May 2021 Manas Gaur, Kaushik Roy, Aditya Sharma, Biplav Srivastava, Amit Sheth

During the ongoing COVID-19 crisis, subreddits on Reddit, such as r/Coronavirus saw a rapid growth in user's requests for help (support seekers - SSs) including individuals with varying professions and experiences with diverse perspectives on care (support providers - SPs).

Natural Language Inference

KI-BERT: Infusing Knowledge Context for Better Language and Domain Understanding

no code implementations9 Apr 2021 Keyur Faldu, Amit Sheth, Prashant Kikani, Hemang Akbari

We take BERT as a baseline model and implement the "Knowledge-Infused BERT" by infusing knowledge context from ConceptNet and WordNet, which significantly outperforms BERT and other recent knowledge-aware BERT variants like ERNIE, SenseBERT, and BERT_CS over eight different subtasks of GLUE benchmark.

Knowledge Graph Embedding Knowledge Graph Embeddings +3

Characterization of Time-variant and Time-invariant Assessment of Suicidality on Reddit using C-SSRS

no code implementations9 Apr 2021 Manas Gaur, Vamsi Aribandi, Amanuel Alambo, Ugur Kursuncu, Krishnaprasad Thirunarayan, Jonanthan Beich, Jyotishman Pathak, Amit Sheth

In this work, we address this knowledge gap by developing deep learning algorithms to assess suicide risk in terms of severity and temporality from Reddit data based on the Columbia Suicide Severity Rating Scale (C-SSRS).

The Duality of Data and Knowledge Across the Three Waves of AI

no code implementations24 Mar 2021 Amit Sheth, Krishnaprasad Thirunarayan

We will draw a parallel with the role of knowledge and experience in human intelligence based on cognitive science, and discuss emerging neuro-symbolic or hybrid AI systems in which knowledge is the critical enabler for combining capabilities of the data-intensive statistical AI systems with those of symbolic AI systems, resulting in more capable AI systems that support more human-like intelligence.

Decision Making

Knowledge Infused Policy Gradients for Adaptive Pandemic Control

no code implementations11 Feb 2021 Kaushik Roy, Qi Zhang, Manas Gaur, Amit Sheth

To this end, we introduce a mathematical framework for KIPG methods that can (a) induce relevant feature counts over multi-relational features of the world, (b) handle latent non-homogeneous counts as hidden variables that are linear combinations of kernelized aggregates over the features, and (b) infuse knowledge as functional constraints in a principled manner.

Decision Making

"Is depression related to cannabis?": A knowledge-infused model for Entity and Relation Extraction with Limited Supervision

no code implementations1 Feb 2021 Kaushik Roy, Usha Lokala, Vedant Khandelwal, Amit Sheth

With strong marketing advocacy of the benefits of cannabis use for improved mental health, cannabis legalization is a priority among legislators.

Contrastive Learning Marketing +1

Semantics of the Black-Box: Can knowledge graphs help make deep learning systems more interpretable and explainable?

no code implementations16 Oct 2020 Manas Gaur, Keyur Faldu, Amit Sheth

The recent series of innovations in deep learning (DL) have shown enormous potential to impact individuals and society, both positively and negatively.

Knowledge Graphs

Assessing the Severity of Health States based on Social Media Posts

no code implementations21 Sep 2020 Shweta Yadav, Joy Prakash Sain, Amit Sheth, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya

The diverse NLU views demonstrate its effectiveness on both the tasks and as well as on the individual disease to assess a user's health.

Multiview Learning Natural Language Understanding

"When they say weed causes depression, but it's your fav antidepressant": Knowledge-aware Attention Framework for Relationship Extraction

no code implementations21 Sep 2020 Shweta Yadav, Usha Lokala, Raminta Daniulaityte, Krishnaprasad Thirunarayan, Francois Lamy, Amit Sheth

In this interdisciplinary study, we demonstrate the value of incorporating domain-specific knowledge in the learning process to identify the relationships between cannabis use and depression.

Position Sentence

Knowledge Graphs and Knowledge Networks: The Story in Brief

no code implementations7 Mar 2020 Amit Sheth, Swati Padhee, Amelie Gyrard

Knowledge Graphs (KGs) represent real-world noisy raw information in a structured form, capturing relationships between entities.

Knowledge Graphs Link Prediction +3

An Evaluation of Knowledge Graph Embeddings for Autonomous Driving Data: Experience and Practice

no code implementations29 Feb 2020 Ruwan Wickramarachchi, Cory Henson, Amit Sheth

With the expectation that neuro-symbolic fusion through KGEs will improve scene understanding, this research explores the generation and evaluation of KGEs for autonomous driving data.

Autonomous Driving Knowledge Graph Embeddings +2

Knowledge Infused Learning (K-IL): Towards Deep Incorporation of Knowledge in Deep Learning

no code implementations1 Dec 2019 Ugur Kursuncu, Manas Gaur, Amit Sheth

Learning the underlying patterns in data goes beyond instance-based generalization to external knowledge represented in structured graphs or networks.

Knowledge Graphs

Modeling Islamist Extremist Communications on Social Media using Contextual Dimensions: Religion, Ideology, and Hate

no code implementations18 Aug 2019 Ugur Kursuncu, Manas Gaur, Carlos Castillo, Amanuel Alambo, K. Thirunarayan, Valerie Shalin, Dilshod Achilov, I. Budak Arpinar, Amit Sheth

Our study makes three contributions to reliable analysis: (i) Development of a computational approach rooted in the contextual dimensions of religion, ideology, and hate that reflects strategies employed by online Islamist extremist groups, (ii) An in-depth analysis of relevant tweet datasets with respect to these dimensions to exclude likely mislabeled users, and (iii) A framework for understanding online radicalization as a process to assist counter-programming.

Multimodal Emotion Classification

no code implementations13 Mar 2019 Anurag Illendula, Amit Sheth

Our experiments demonstrate that the three modalities (text, emoji and images) encode different information to express emotion and therefore can complement each other.

Classification Emotion Classification +1

Fusing Visual, Textual and Connectivity Clues for Studying Mental Health

no code implementations19 Feb 2019 Amir Hossein Yazdavar, Mohammad Saeid Mahdavinejad, Goonmeet Bajaj, William Romine, Amirhassan Monadjemi, Krishnaprasad Thirunarayan, Amit Sheth, Jyotishman Pathak

With ubiquity of social media platforms, millions of people are sharing their online persona by expressing their thoughts, moods, emotions, feelings, and even their daily struggles with mental health issues voluntarily and publicly on social media.

Analyzing and learning the language for different types of harassment

no code implementations1 Nov 2018 Mohammadreza Rezvan, Saeedeh Shekarpour, Faisal Alshargi, Krishnaprasad Thirunarayan, Valerie L. Shalin, Amit Sheth

In this paper, we introduce the notion of contextual type to harassment involving five categories: (i) sexual, (ii) racial, (iii) appearance-related, (iv) intellectual and (v) political.

Vocal Bursts Type Prediction

Leveraging Medical Sentiment to Understand Patients Health on Social Media

no code implementations30 Jul 2018 Shweta Yadav, Joy Sain, Amit Sheth, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya

A large percentage of this population is actively engaged in health social networks to share health-related information.

A Practical Incremental Learning Framework For Sparse Entity Extraction

1 code implementation COLING 2018 Hussein S. Al-Olimat, Steven Gustafson, Jason Mackay, Krishnaprasad Thirunarayan, Amit Sheth

This work addresses challenges arising from extracting entities from textual data, including the high cost of data annotation, model accuracy, selecting appropriate evaluation criteria, and the overall quality of annotation.

Active Learning Entity Extraction using GAN +1

Predictive Analysis on Twitter: Techniques and Applications

1 code implementation6 Jun 2018 Ugur Kursuncu, Manas Gaur, Usha Lokala, Krishnaprasad Thirunarayan, Amit Sheth, I. Budak Arpinar

Predictive analysis of social media data has attracted considerable attention from the research community as well as the business world because of the essential and actionable information it can provide.

Social and Information Networks

Multi-Task Learning Framework for Mining Crowd Intelligence towards Clinical Treatment

no code implementations NAACL 2018 Shweta Yadav, Asif Ekbal, Sriparna Saha, Pushpak Bhattacharyya, Amit Sheth

In this paper, we adopt a novel adversarial learning approach for our multi-task learning framework to learn the sentiment{'}s strengths expressed in a medical blog.

General Classification Multi-Task Learning +1

Concept2vec: Metrics for Evaluating Quality of Embeddings for Ontological Concepts

1 code implementation12 Mar 2018 Faisal Alshargi, Saeedeh Shekarpour, Tommaso Soru, Amit Sheth

This deficiency is further sensed with respect to embeddings generated for structured data because there are no concrete evaluation metrics measuring the quality of the encoded structure as well as semantic patterns in the embedding space.

A Quality Type-aware Annotated Corpus and Lexicon for Harassment Research

no code implementations26 Feb 2018 Mohammadreza Rezvan, Saeedeh Shekarpour, Lakshika Balasuriya, Krishnaprasad Thirunarayan, Valerie Shalin, Amit Sheth

In this paper, we publish first, a quality annotated corpus and second, an offensive words lexicon capturing different types type of harassment as (i) sexual harassment, (ii) racial harassment, (iii) appearance-related harassment, (iv) intellectual harassment, and (v) political harassment. We crawled data from Twitter using our offensive lexicon.

Vocal Bursts Type Prediction

How will the Internet of Things enable Augmented Personalized Health?

no code implementations31 Dec 2017 Amit Sheth, Utkarshani Jaimini, Hong Yung Yip

It is usually necessary to look at that individual's clinical record and behavioral information, as well as social and environmental information affecting that individual.

Clinical Knowledge Management

Semi-Supervised Approach to Monitoring Clinical Depressive Symptoms in Social Media

no code implementations16 Oct 2017 Amir Hossein Yazdavar, Hussein S. Al-Olimat, Monireh Ebrahimi, Goonmeet Bajaj, Tanvi Banerjee, Krishnaprasad Thirunarayan, Jyotishman Pathak, Amit Sheth

With the rise of social media, millions of people are routinely expressing their moods, feelings, and daily struggles with mental health issues on social media platforms like Twitter.

On the Challenges of Sentiment Analysis for Dynamic Events

no code implementations6 Oct 2017 Monireh Ebrahimi, Amir Hossein Yazdavar, Amit Sheth

With the proliferation of social media over the last decade, determining people's attitude with respect to a specific topic, document, interaction or events has fueled research interest in natural language processing and introduced a new channel called sentiment and emotion analysis.

Emotion Recognition Marketing +1

Location Name Extraction from Targeted Text Streams using Gazetteer-based Statistical Language Models

1 code implementation COLING 2018 Hussein S. Al-Olimat, Krishnaprasad Thirunarayan, Valerie Shalin, Amit Sheth

Extracting location names from informal and unstructured social media data requires the identification of referent boundaries and partitioning compound names.

Language Modelling

Implicit Entity Linking in Tweets

no code implementations26 Jul 2017 Sujan Perera, Pablo N. Mendes, Adarsh Alex, Amit Sheth, Krishnaprasad Thirunarayan

We demonstrate how to use these models to perform implicit entity linking on a ground truth dataset with 397 tweets from two domains, namely, Movie and Book.

Entity Linking Natural Language Understanding

A Semantics-Based Measure of Emoji Similarity

2 code implementations14 Jul 2017 Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran

This paper presents a comprehensive analysis of the semantic similarity of emoji through embedding models that are learned over machine-readable emoji meanings in the EmojiNet knowledge base.

Semantic Similarity Semantic Textual Similarity +1

EmojiNet: An Open Service and API for Emoji Sense Discovery

no code implementations14 Jul 2017 Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran

This paper presents the release of EmojiNet, the largest machine-readable emoji sense inventory that links Unicode emoji representations to their English meanings extracted from the Web.

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

no code implementations14 Jul 2017 Amit Sheth, Sujan Perera, Sanjaya Wijeratne, Krishnaprasad Thirunarayan

Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.

Logical Inferences with Contexts of RDF Triples

no code implementations20 Jan 2017 Vinh Nguyen, Amit Sheth

This formal semantics also allows us to derive a new set of entailment rules that could entail new contextual triples about triples.

World Knowledge

Finding Street Gang Members on Twitter

no code implementations29 Oct 2016 Lakshika Balasuriya, Sanjaya Wijeratne, Derek Doran, Amit Sheth

A review of these profiles establishes differences in the language, images, YouTube links, and emojis gang members use compared to the rest of the Twitter population.

EmojiNet: Building a Machine Readable Sense Inventory for Emoji

no code implementations25 Oct 2016 Sanjaya Wijeratne, Lakshika Balasuriya, Amit Sheth, Derek Doran

It is automatically constructed by integrating multiple emoji resources with BabelNet, which is the most comprehensive multilingual sense inventory available to date.

Word Sense Disambiguation

Semantic, Cognitive, and Perceptual Computing: Advances toward Computing for Human Experience

no code implementations20 Oct 2015 Amit Sheth, Pramod Anantharam, Cory Henson

Toward this goal, we discuss computing paradigms of semantic computing, cognitive computing, and an emerging aspect of computing, which we call perceptual computing.


On Reasoning with RDF Statements about Statements using Singleton Property Triples

no code implementations15 Sep 2015 Vinh Nguyen, Olivier Bodenreider, Krishnaprasad Thirunarayan, Gang Fu, Evan Bolton, Núria Queralt Rosinach, Laura I. Furlong, Michel Dumontier, Amit Sheth

If the singleton property triples describe a data triple, then how can a reasoner infer this data triple from the singleton property triples?

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